While rule-based chatbots have been growing for a decade, they’ve been falling short of delivering and managing user context. With rapid development in Conversational AI, the need of the hour is to deliver bots that don’t just process text but also understand the motive of the user, remember the context and then scale conversations. However, implementing custom machine learning models in order to build these intuitive chatbots can be tedious and more often than not, an inaccurate process.
How then, in the face of the growing demand for smarter conversational AI do we implement a backend that is both - easy to train and scalable? The answer to these difficult questions is pretty simple - Rasa. Before we dive into what Rasa is and how it can make our mediocre chatbots truly intuitive and transform the user’s experience, let us first understand what Natural Language Processing (NLP) and Understanding (NLU) is and why it has become an indispensable requirement for conversational interfaces today.
To explain this broadly NLP is the machine’s ability to accept what is being said in natural human language, interpret it and take an appropriate course of action. At its core, it takes unstructured text as input and extracts meaning from it upon which the machine takes further action.
NLU or Natural Language Understanding is a subset of this machine ability. It deals with the best practices that can be used to handle unstructured input and extract it in an appropriate structural form that machines can understand and derive meaning from. Humans are adept at understanding mispronunciations, swapped words, contractions, colloquialisms, and other quirks, machines need to be trained to extract meaning from such humane inputs.
This is where Rasa comes into the picture. It comes with two key components -The natural language understanding component Rasa NLU and the dialogue management component the Rasa Core.
Contextual virtual assistants are becoming increasingly domain-specific. Rasa NLU helps us deliver this domain-specific solution for building contextual AI assistants trained with extensive domain knowledge. Rasa NLU uses intents and entities extraction to perform this recognition from the user input.
Intents - It is the user’s purpose. Intents help the machine identify the user’s intention through what they have said.
Entity - These are supportive elements that modify an intent. Entities can also be referred to as metadata about intents.
Rasa NLU is trained with a list of domain-specific intents in order to develop a self-learning model that improves with time. You can think of this Rasa component as “a set of high-level APIs for building a language parser suited to your use case using existing NLP and ML libraries.”
One of the key components of this framework is the pipeline configuration used for processing your user input using a pipeline of successively placed components. While Rasa provides preconfigured pipelines, it also enables users to define their own pipeline structure to suit their requirements. It is a vital feature that contributes to the customizability of Rasa NLU in order to process your user input using components that best fit the conversations and utterances of your end customer.
As Rasa itself puts it -
“A pipeline defines different components which process a user message sequentially and ultimately lead to the classification of user messages into intents and the extraction of entities.”
Leveraging Rasa @ Kevit
At Kevit our expert developers have already built dozens of chatbots and frameworks spanning various industries. We believe in providing a scalable framework and opportunity for our clients to build the conversational AI of the future for their consumers.
We have adapted the Rasa NLU in order to provide our customers with the flexibility of building bots that are truly intuitive. Through extensive research, we build pipelines that custom fit your industry requirements and build scalable chatbots from there on. We believe in leveraging this revolutionary technology to build conversational interfaces that can -
- Identify user sentiment
- Seamlessly handle small talk and fallback
- Design conversation models that intelligently adjust to user input
- Handle unpredictability and much more
While our developers are equipped with an array of technical languages and technologies, we use Rasa NLU as a valuable component in our systems to leverage the ML algorithms that Rasa implements. We exercise a contemporary approach to build bots that are unlike any other available in the market.
With the growing competition of implementing chatbots for every industry imaginable, make yours truly stand out from the rest. Contact us at email@example.com to know more about intelligent chatbots and how they can escalate your customer engagement and lead generation and visit us here.